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IEEE International Conference on Intelligent computer Communication and Processing (ICCP)

作者： Mahdi Bahaghighat Fereshteh Abedini Misak S’hoyan Arthur-Jozsef Molnar Department of Electrical Engineering Raja University Qazvin Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Information Security National Polytechnic University of Armenia Yerevan Iran Faculty of Mathematics and Computer Science Babeş-Bolyai University Cluj-Napoca Romania

Nowadays, the growth and development of artificial intelligence is more evident than any other time. Consequently, the development of machine vision systems in the industry has been studied extensively. The provision of a high-level quality control system based on vision inspection in production lines is an important issue. It not only can increase the efficiency, but also derives the necessary tools to gather information about technical errors and faults in the target system. In this paper, we focus on the vision inspection of bottle caps in drink factories using convolutional neural networks. There are different cap encapsulation statuses which are classified as Normal Cap, Unfixed Cap, and No Cap. According to these classes, we can make a decision between either acceptance or rejection of a given bottle. Achieved results show that our optimized fine-tuning method based on the VGG-19 as an End-to-End deep learning approach can lead to more than 99% accuracy.

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Adversarial Attacks on Remote User Authentication Using Behavioural Mouse Dynamics

arXiv

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arXiv 2019年

作者： Tan, Yi Xiang Marcus Iacovazzi, Alfonso Homoliak, Ivan Elovici, Yuval Binder, Alexander ST Engineering Electronics-SUTD Cyber Security Laboratory Pillar Singapore University of Technology and Design Singapore Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel Deutsche Telekom Innovation Laboratories Ben-Gurion University of the Negev Beer-Sheva Israel

—Mouse dynamics is a potential means of authenticating users. Typically, the authentication process is based on classical machine learning techniques, but recently, deep learning techniques have been introduced for this purpose. Although prior research has demonstrated how machine learning and deep learning algorithms can be bypassed by carefully crafted adversarial samples, there has been very little research performed on the topic of behavioural biometrics in the adversarial domain. In an attempt to address this gap, we built a set of attacks, which are applications of several generative approaches, to construct adversarial mouse trajectories that bypass authentication models. These generated mouse sequences will serve as the adversarial samples in the context of our experiments. We also present an analysis of the attack approaches we explored, explaining their limitations. In contrast to previous work, we consider the attacks in a more realistic and challenging setting in which an attacker has access to recorded user data but does not have access to the authentication model or its outputs. We explore three different attack strategies: 1) statistics-based, 2) imitation-based, and 3) surrogate-based;we show that they are able to evade the functionality of the authentication models, thereby impacting their robustness adversely. We show that imitation-based attacks often perform better than surrogate-based attacks, unless, however, the attacker can guess the architecture of the authentication model. In such cases, we propose a potential detection mechanism against surrogate-based attacks. Copyright © 2019, The Authors. All rights reserved.

关键词： Learning algorithms

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Erratum to “High-Efficiency Min-Entropy Estimation Based on Neural Network for Random Number Generators”

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security and Communication Networks 2020年第1期2020卷

作者： Na Lv Tianyu Chen Shuangyi Zhu Jing Yang Yuan Ma Jiwu Jing Jingqiang Lin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 *** Data Assurance and Communications Security Research Center Chinese Academy of Sciences Beijing 100093 *** School of Cyber Security University of Chinese Academy of Sciences Beijing 100093 *** China Information Technology Security Evaluation Center Beijing 100085 *** School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100093 ***

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A Note on Self-Accelerating Olver and Olver-Gauss Beams

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Optics and Photonics Journal 2019年第1期9卷 1-7页

作者： Ioannis M. Besieris Amr M. Shaarawi Bradley Davis The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Physics The American University in Cairo New Cairo Egypt Ted and Karyn Hume Center for National Security and Technology Virginia Polytechnic Institute and State University Blacksburg VA USA

The recent introduction by Belafhal et al. [Opt. and Photon. J. 5, 234-246 (2015)] of mth-order Olver beams as a novel class of self-accelerating nondiffracting solutions to the paraxial equation is a direct contradiction to the seminal work of Berry and Balazs who determined that the infinite-energy Airy wave packet is the only accelerating nondiffracting solution to the (1 + 1)D Schrödinger equation. It is shown in this note that the work of Belafhal et al. is valid only for m=0, which coincides with the Airy solution.

关键词： Nondiffracting Waves Nonlinearly Self-Bending Beams Airy Beam Accelerating Olver and Olver-Gauss Beams

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Digital Contact Tracing Service: An improved decentralized design for privacy and effectiveness

arXiv

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arXiv 2020年

作者： Holzapfel, Kilian Karl, Martina Lotz, Linus Carle, Georg Djeffal, Christian Fruck, Christian Haack, Christian Heckmann, Dirk Kindt, Philipp H. Köppl, Michael Krause, Patrick Shtembari, Lolian Marx, Lorenz Meighen-Berger, Stephan Neumair, Birgit Neumair, Matthias Pollmann, Julia Pollmann, Tina Resconi, Elisa Schönert, Stefan Turcati, Andrea Wiesinger, Christoph Zattera, Giovanni Allan, Christopher Barco, Esteban Bitterschulte, Kai Buchwald, Jörn Fischer, Clara Gampe, Judith Häcker, Martin Islami, Jasin Pomplun, Anatol Preisner, Sebastian Quast, Nele Romberg, Christian Steinlehner, Christoph Ziehm, Tjark Department of Physics Technical University of Munich Garching bei München Germany Max Planck Institute for Physics Munich Germany Department of Informatics Technical University of Munich Garching bei München Germany TUM School of Governance Munich Center for Technology in Society Technical University of Munich Munich Germany TUM School of Governance Department of Informatics Chair of Law and Security in Digital Transformation Technical University of Munich Munich Germany Department of Electrical and Computer Engineering Chair of Real-Time Computer Systems Technical University of Munich Munich Germany ito-app Ober-Olm Germany

We propose a decentralized digital contact tracing service that preserves the users’ privacy by design while complying to the highest security standards. Our approach is based on Bluetooth and measures actual encounters of people, the contact time period, and estimates the proximity of the contact. We trace the users’ contacts and the possible spread of infectious diseases while preventing location tracking of users, protecting their data and identity. We verify and improve the impact of tracking based on epidemiological models. We compare a centralized and decentralized approach on a legal perspective and find a decentralized approach preferable considering proportionality and data minimization. Copyright © 2020, The Authors. All rights reserved.

关键词： Bluetooth

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Privacy-preserving medical treatment system through nondeterministic finite automata

arXiv

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arXiv 2020年

作者： Yang, Yang Deng, Robert H. Liu, Ximeng Wu, Yongdong Weng, Jian Zheng, Xianghan Rong, Chunming School of Information Systems Singapore Management University Singapore College of Mathematics and Computer Science Fuzhou University Fujian China State Key Laboratory of Integrated Services Networks Xidian University Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Systems Singapore Management University Singapore Singapore Department of Computer Science Jinan University Guangdong China College of Mathematics and Computer Science Fuzhou University Fujian China Department of Electronic Engineering and Computer Science University of Stavanger Norway MingByte Technology Qingdao China

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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Research and Implementation of Precision Marketing System Based on Big Data Analysis 5

Research and Implementation of Precision Marketing System Ba...

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2018 5th International Conference on Advanced Composite Materials and Manufacturing engineering, ACMME 2018

作者： Wang, Ling Xu, Haochen Cao, Yan School of Cyber Security Gansu Institute of Political Science and Law Gansu730070 China Hefei University of Technology Department of Business Xuancheng242000 China School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China

For the data analysis in the mass data, the data processing ability of the recommendation system is very high. Various kinds of high performance computing frameworks are produced with the Spark, and the computing ability is very strong. The application of Spark to recommendation system will greatly improve the efficiency of operation. This paper discusses the processing technology of big data, and introduces some commonly used recommendation algorithms, and designs a recommendation system combined with big data. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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Erratum to: The formation mechanism of aqueous hydrogen peroxide in a plasma-liquid system with liquid as the anode

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The European Physical Journal D 2020年第7期74卷 1-2页

作者： Lin, Jiao He, Xinyi Chen, Qiang Xiong, Qing Li, Junshuai Wang, Xin Chen, Guolong Liu, Qing Huo Ostrikov, Kostya (Ken) Institute of Electromagnetics and Acoustics Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Department of Electronic Science Xiamen University Xiamen P.R. China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing P.R. China Key Laboratory of Special Function Materials & Structure Design of the Ministry of Education School of Physical Science & Technology Lanzhou University Lanzhou P.R. China Department of Electrical and Computer Engineering Duke University Durham USA Institute for Future Environments and School of Chemistry Physics and Mechanical Engineering Queensland University of Technology Brisbane Australia CSIRO-QUT Joint Sustainable Processes and Devices Laboratory Commonwealth Scientific and Industrial Research Organization Lindfield Australia

We find that we made a mistake in the estimation of the H2O2 concentration by the colorimetric method in our previous paper (J. Lin et al., Eur. Phys. J. D (2020) 74: 80) [1].

关键词： Atomic and Molecular Collisions

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Delay-Aware Adaptive Wireless Video Streaming in Edge Computing Assisted Ultra-Dense Networks

Delay-Aware Adaptive Wireless Video Streaming in Edge Comput...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Liang Li Ronghui Hou Ruoguang Li Hui Li Miao Pan Zhu Han School of Cyber Engineering Xidian University P. R. China Science and Technology on Communication Networks Laboratory School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing P.R.China Department of Electrical and Computer Engineering University of Houston Houston TX

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Server and Network Assisted Dynamic adaptive streaming over HTTP (SAND) is a promising technology to cope with the dramatic increase in video streaming traffic. The new emerging Mobile Edge Computing (MEC) paradigm may further facilitate bitrate adaptation and video transcoding in a SAND system with the help of local edge servers. A critical issue in MEC-SAND framework is to guarantee Quality of Experience (QoE) for clients while achieving efficient utilization of edging network resources. In this paper, we aim to develop an adaptive video delivery scheme to minimize the delay in MEC assisted ultra-dense networks. In our scheme, each client is mapped to a server that better fits its requirements and transmission condition, and a bitrate selection mechanism is exploited to decide the best video version for the client. Besides, time-consuming transcoding tasks are carefully scheduled considering edge computing capacity. We formulate a Mix-Integer Non-Linear Programming (MINLP) problem to jointly determine cell association, bitrate adaption, and computing resource allocation. We exploit the generalized Benders decomposition method to reduce the solving complexity of the formulated problem. Numerical results validate the effectiveness and efficiency of the proposed scheme.

关键词： Bit rate Servers Streaming media Transcoding Delays Quality of experience

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Deep Learning for Password Guessing and Password Strength Evaluation, A Survey

Deep Learning for Password Guessing and Password Strength Ev...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Tao Zhang Zelei Cheng Yi Qin Qiang Li Lin Shi Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi University for Nationalities Nanning China Department of Computer and Information Technology Purdue University West Lafayette USA PLA Information Engineering University Zhengzhou China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Cloud Computing and Big Data Research Institute China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.

关键词： Deep learning Training Transfer learning security Gallium nitride Password Text processing

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